Google Maps provides a street-by-street view of the world based on satellite imagery, and uses facial recognition algorithms to process the mosaic. These algorithms, which utilize deep learning, play an important role in a variety of fields, including healthcare, self-driving cars, and smart grid systems, and are combined with numerical analysis techniques to increase efficiency and accuracy.
Google Maps is a mapping service provided by Google that provides a street-by-street view of the world. The service is based on satellite imagery. These photos inevitably show the faces of people walking down the street, which raised privacy concerns and eventually led to the mosaicking of all of them. Google Maps has evolved steadily since its inception, and now offers the ability to explore not only roads, but also the inside of buildings, natural landscapes, and even underwater. This has made life a lot easier for users, allowing them to plan trips or explore specific places in advance.
Could this be done by humans? Of course, it could, but it’s hard to keep track of 150 million square kilometers of the Earth’s land mass. A similar problem exists on social media. If you’ve ever been on Facebook, you’ve probably seen a square window pop up in a photo with your friends’ faces, prompting you to tag them. The algorithms that solve these problems are called facial recognition algorithms. In general, algorithms for identifying objects in photos or videos are studied not only in computer science, but also in my major, electrical engineering.
There are many different methods of facial recognition algorithms, but the most popular method is to train a machine using a database. This method is also known as deep learning. Deep learning is a branch of machine learning that uses artificial neural networks to learn patterns from large amounts of data. For example, in face recognition, if you have a large collection of photos of people’s faces, a computer can use a deep learning algorithm to determine how similar a given photo is to the ones it has, and tell you whether it has a face or not. This can be used to identify problematic body tissues in medical imaging, or applied to the eyes of human-like robots. In the medical field, for example, diagnostic systems utilizing deep learning are contributing significantly to the early detection of diseases, which has helped save many patients’ lives.
Advanced algorithms like deep learning are also playing a big role in other fields. Self-driving cars need to recognize their surroundings, classify objects, and react in real time. This is where image recognition technology, similar to facial recognition technology, comes into play. Computers analyze images from cameras in real time to recognize pedestrians, vehicles, road signs, etc. and adjust the driving path accordingly. Advances in autonomous driving technology will help reduce traffic accidents, smooth traffic flow, and contribute to the smart cities of the future.
However, there are also a variety of problems that arise when object recognition algorithms are put into practice. In the movie ‘The Imitation Game,’ Alan Turing builds a decoding machine, but is worried because the results that should have come out in a day don’t come out for several days. In other words, it is necessary to consider how well it recognizes, as well as the speed of the algorithm and the burden on the hardware. This remains one of the biggest challenges for technologies like deep learning to be commercialized.
I am an electrical engineering major with a minor in mathematics, and I am particularly interested in the field of numerical analysis. This field studies how to find the fastest way to answer a problem with a certain accuracy when dealing with large amounts of numerical data. Applying mathematical theory, the amount of information to be dealt with is large, so each meaningful value is represented only numerically using a matrix. Thus, the simplest problem is to solve Ax=b. The easiest way to think about this is to multiply the inverse of A by b to get x. However, the time it would take a computer to solve this would be very long. There are millions of weather variables needed to predict the weather, and calculating them this way would take longer than the age of the universe, even on the fastest computers in existence. However, we actually hear weather forecasts that are more or less accurate by applying other methods.
Another important issue in the field of numerical analysis is stability. If a numerical algorithm does not converge or has a large error, the results can be significantly skewed. For example, in financial modeling, using the wrong numerical methods can lead to huge economic losses. Therefore, numerical analysis researchers are constantly developing new methods to ensure the stability and accuracy of their algorithms.
As the quality of photos and videos gets better and better, the amount of data that computers need to recognize objects is growing rapidly. Therefore, the need to understand mathematical tools will only increase in the future. Furthermore, these numerical analysis techniques play an essential role in a variety of fields, including economic forecasting, genomic research, aerospace engineering, and more. Furthermore, advanced sciences such as nanotechnology will become increasingly important as they require accurate calculations on a small scale. In the end, ensuring that data is both accurate and efficient will be key to future technological advances. The convergence of electrical engineering and mathematical tools will play an increasingly important role in this process. For example, smart grid systems monitor and predict energy consumption in real time to enable efficient energy distribution. This can contribute significantly not only to energy savings but also to environmental protection. We look forward to seeing how these technologies will change our daily lives in the future.
